When it comes to trading cards (sports, gaming and otherwise), this isn’t the only price guide out there. Not by a long shot. And, yet, I think you should keep checking in with us. Maybe its a lack of humility, but here are our concrete advantages as I see them:
The Expert & the Data
(1) Data driven results from actual auction results crush bias. If you aren’t presenting data-driven prices, you are doing expert opinions. Now, to be certain, expert opinions are great to have. (Although, ideally, resources should be clear about the criteria and data that experts gather to inform their estimates.) But there are two disadvantages. First, experts are also experts because they love the hobby. That’s doesn’t have to be a bad thing, but it is hard to not let that affection creep into evaluations. By sticking with hard stats, you avoid this. Second, expert opinions can be swayed by asking prices and ideologies (i.e., the potential use of untested assumptions about what cards A, B and C should be worth, given my theory that X, Y and Z are the real drivers of value). But asking prices are not market prices. An item can sit in a shop forever if priced too high. And theories are just theories until they are not falsified with empirical tests. Auctions, whatever their faults, solve a lot of problems on these fronts: what is a card actually proven to be worth, given a reasonable time horizon to turn it over?
Including Inference in Prediction
(2) Statistical inference lets us understand what hasn’t yet happened. There are a lot of rare cards out there. Many have sold. Many have not. Some have sold but, like, 10 years ago. Price guides that don’t do statistical inference, but want to do data, can only give us historical averages of just those cards that they have data for. But, by making some assumptions (that are hopefully reasonable), we can say a lot about cards for which we do not, yet, have any sales data.
It is for this reason that I argue inferences are crucially important to building a price guide. Probability-wise, the more enticing a card’s value, the less likely there is any public records of its sales price. After all, the most interesting cards that you probably want to know about (because they are super rare!) are exactly those you probably don’t have strong market data on (why? because they are super rare!)
Inference allows us to take characteristics of cards that did sell, and make reasonable statements about what that implies for other cards (that have never sold) given how similar or different they are from it. So, let’s say we have prices for a bunch of Wayne Gretzky autographs, but we want to know the value of Connor McDavid autographs… or Michael Jordan, or Mike Trout, or Peyton Manning, etc.). Well, we can ask what what multiplier on value exists for two comparable cards for each player, and then apply it to autographs. Obviously, a good model will have more than that going on to conduct its estimations… but that hopefully gives an initial idea of how these models work. This inference also allows us to give updated estimates that reflect the current times. So, say you do know what a particular card previously sold for, long, long ago… well, without inference, that doesn’t mean you know what it is worth today. But, again, here inference comes through for us. We can use trends in time, about the hobby in general, and about characteristics particular to an individual card (e.g., has the player’s values, in general, been going up or down? Have rookie cards been getting a greater or lesser premium of late? ), to put down a reasonable guess on its value today.
SCVs Upshot
Some fellows are doing the first, and others are doing the second, but not many are making a point of doing both. That’s what I hope to offer… Also, the whole thing being free don’t hurt either.